Stability and Synchronization of Discrete-Time Neural Networks With Switching Parameters and Time-Varying Delays
Stability and Synchronization of Discrete-Time Neural Networks With Switching Parameters and Time-Varying Delays
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DOI：
10.1109/tnnls.2013.2271046
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发表时间：
2013-07
影响因子：
10.4
通讯作者：
Ligang Wu;Zhiguang Feng;J. Lam
中科院分区：
文献类型：
--
作者：
Ligang Wu;Zhiguang Feng;J. Lam
研究了离散时间切换时滞神经网络的指数稳定性分析和同步问题。利用平均停留时间方法和分段Lyapunov函数技术，给出了具有时滞的切换神经网络具有指数稳定性的充分条件。利用延迟划分法和自由加权矩阵技术，降低了所得结果的保守性。此外，明确给出了衰减估计，并解决了同步问题。本文报告的结果不仅依赖于延迟，而且依赖于分区，旨在降低保守性。通过数值算例验证了所得理论结果的有效性。
This paper is concerned with the problems of exponential stability analysis and synchronization of discrete-time switched delayed neural networks. Using the average dwell time approach together with the piecewise Lyapunov function technique, sufficient conditions are proposed to guarantee the exponential stability for the switched neural networks with time-delays. Benefitting from the delay partitioning method and the free-weighting matrix technique, the conservatism of the obtained results is reduced. In addition, the decay estimates are explicitly given and the synchronization problem is solved. The results reported in this paper not only depend upon the delay, but also depend upon the partitioning, which aims at reducing the conservatism. Numerical examples are presented to demonstrate the usefulness of the derived theoretical results.